Specifically, following convention [1]}, we first employ a projection head consisting of a \(\operatorname{FC}\)  layer and a \(\operatorname{L2}\)  normalization for mapping the embed feature \(E\)  into a low-dimensional unit hypersphere.
We express the output as \(Z = [z_1, .., z_t] \in \mathrm {R}^{T \times D_z}\) . Here \(D_z\)  is the number of channels with \(D_z < D_f\) .
Likewise, we can also obtain the compact representations of \(E^{\prime }\)  denoted by \(Z^{\prime } = [z^{\prime }_1, .., z^{\prime }_t]\) .
